Analysis of Groundwater level in Kouhdasht plain of Lorestan using Metaheuristic Models

Document Type : Research Paper

Authors

1 Research Assistant, Department of Soil Conservation and Watershed Management, Lorestan Province Agriculture and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Khorramabad, Iran

2 PhD in Water Sciences and Engineering, Department of Soil Conservation and Watershed Management, Lorestan Province Agriculture and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Khorramabad, Iran

3 Research instructor, Department of Soil Conservation and Watershed Management, Lorestan Province Agriculture and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Khorramabad, Iran

Abstract

Prediction of groundwater levels using machine learning techniques has gained substantial attention over the past few decades. Several researchers have reported the advances in this field and provided clear understanding of the state-of-the-art machine learning models implemented for GWL modeling.In this research, a new hybrid model based on artificial neural network approaches has been developed to estimate the groundwater level. For this purpose, three optimization algorithms, including wavelet, creative gunner, and black widow spider, were employed for modeling the groundwater level. The study utilized statistical data from four piezometers in the Kouhdasht plain located in Lorestan province, Iran, as a case study over five combined scenarios of input parameters from 2002 to 2022. To evaluate the performance of the models, correlation coefficient, root mean square error, mean absolute error, and Nash-Sutcliffe efficiency coefficient were used as assessment criteria. Additionally, time series charts and box plots were employed to analyze the model results. The findings indicated that the combined scenarios in the models under consideration improved the model’s performance. Moreover, the evaluation results showed that the wavelet-support vector regression model exhibited higher accuracy than the other models across all examined piezometric wells. Overall, the results demonstrated that the use of intelligent models based on the hybrid approach of artificial neural networks can be an effective factor in water resource management.

Keywords

Main Subjects

References

Alfarrah, N., and Walraevens, K. 2018. Groundwater overexploitation and seawater intrusion in coastal areas of arid and semi-arid regions. Water. 10(2), 143-154.
Barzegar, R., Fijani, E., Moghaddam, A. A., and Tziritis, E. 2017. Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models. Science of The Total Environment. 599(3), 20–31.
Bovolo, C. I., Parkin, G., and Sophocleous, M. 2009. Groundwater resources, climate and vulnerability. Environmental Research Letters . 4(1), 125-142.
Brunner, P., and Simmons, C.T. 2012. HydroGeoSphere: a fully integrated, physically based hydrological model. Ground water. 50(2), 170–176.
Dehghani, R., and  Dehghani, F. 2022.  Application of wavelet neural network in estimation of average air-temperature. Environmental Resources Research.10(2),1-10
Dehghani, R., and  Torabi Poudeh, H. 2022. The effect of climate change on groundwater level and its prediction using modern meta- heuristic model. Groundwater for Sustainable Development. 16(4),224-238.
Dehghani, R., and Torabi Poudeh, H. 2021. Application of novel hybrid artificial intelligence algorithms to groundwater simulation. International Journal of Environmental Science and Technology. 19(3),4351-4368.
Dehghani, R., Babaali, H. R., and Zeidalinejad, N. 2022. Evaluation of Statistical Models and Modern Hybrid Artificial Intelligence in Simulation of Runoff Precipitation Process. Sustainable Water Resources Management . 8(5), 154-176.
Dehghani, R., Torabi Poudeh, H., and  Izadi, Z. 2021. Dissolved oxygen concentration predictions for running waters with using hybrid machine learning techniques. Modeling Earth Systems and Environment. 8(2),2599-2613.
Elmotawakkil, A.,Sadiki, A., and  Enneya, N. 2024. Predicting groundwater level based on remote sensing and machine learning: a case study in the Rabat-Kénitra region .Journal of Hydroinformatics . 26 (10), 2639–2667
Eriksson, E. 1970. Groundwater time series: an exercise in stochastic hydrology. Hydrology Research. 1(1), 181–205.
Feng, F., Ghorbani, H., and  Radwan, A. 2024. Predicting groundwater level using traditional and deep machine learning algorithms.Frontiers in Environmental Science.12(3), 442-461.
Hay, J. E., and Mimura, N. 2005. Sea-level rise: implications for water resources management. Mitigation and Adaptation Strategies for Global Change.10(3), 717–737.
Hornik, K. 1998. Multilayer feed-forward networks are universal approximators, Neural Networks . 2(5), 359–366.
Hughes, J. D., Russcher, M. J., Langevin, C. D., Morway, E. D., and McDonald, R.R. 2022. The MODFLOW Application Programming Interface for simulation control and software interoperability. Environmental Modelling & Software. 148(4), 105-122.
Kang, D.-h., So, Y. H., Kim, I. K., Oh, S.-b., Kim, S., and Kim, B.W. 2017. Groundwater flow and water budget analyses using HydroGeoSphere model at the facility agricultural complex. Engineering Geology. 27(2), 313–322.
Khan, J., Lee, E., Balobaid, A. S., and Kim, K. 2023. A comprehensive review of conventional, machine leaning, and deep learning models for groundwater level (GWL) forecasting. Applied Sciences . 13(2), 274-289.
Kisi, O., Karahan, M., and Sen, Z. 2006. River suspended sediment modeling using fuzzy logic approach. Hydrological Processes. 20(2), 4351-4362.
Ma, J., Liu, H., Shi, Y., and Zhang, H. 2022. Study on the numerical simulation of groundwater “drainage and recharge in open pit coal mine based on FEFLOW,” in 2022 8th International Conference on Hydraulic and Civil Engineering: Deep Space Intelligent Development and Utilization Forum (ICHCE). Xi’an, China, November, 1–8.
Mirboluki, A., Mehraein, M., Kisi, O., Kuriqi, A., and Barati, R. 2024.Groundwater level estimation using improved deep learning and soft computing methods. Earth Science Informatics.17(2), 2587–2608
Mirzania, E., Ghorbani, M.A., and Asadi, E. 2023.Enhancement groundwater level prediction using hybrid ANN-HHO model: case study (Shabestar Plain in Iran).Arabian Journal of Geosciences.16(4), 464-482.
Nagy, H., Watanabe, K., and  Hirano, M. 2002. Prediction of sediment load concentration in rivers using artificial neural network model. Journal of Hydraulics Engineering. 128(4), 558-559.
Nourani, V., Kisi, Ö., and Komasi, M. 2011. Two hybrid artificial intelligence approaches for modeling rainfall–runoff process. Journal of Hydrology. 402(2), 41–59.
Nourani, V., Tajbakhsh, A.D., and  Molajou, A. 2018. Data mining based on wavelet and decision tree for rainfall-runoff simulation. Hydrology Research. 50:75–84. Nourani, V., Alami, M. T., Aminfar, M. H. (2009). A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Engineering Applications of Artificial Intelligence . 22(2),466–472.
Pijarski, P., and Kacejko, P. 2019. A new metaheuristic optimization method: the algorithm of the innovative gunner (AIG). Engineering Optimization. 51(12),2049-2068.
Pragnaditya, M., Abhijit, M., Bhanja, S. N., Kumar, R. R., Sudeshna, S., and Anwar, Z. 2021. Machine-learning-based regional-scale groundwater level prediction using GRACE. Hydrogeology Journal. 29(3), 1027–1042.
Priyan, K. 2021. Issues and challenges of groundwater and surface water management in semi-arid regions. Groundwater Resources Development and Planning in the Semi-Arid Region. 4(1),1–17.
Russo, S.L., and Taddia, G. 2009. Groundwater in the urban environment: management needs and planning strategies. American Journal of Environmental Sciences. 5(2), 494–500.
Saroughi, M., Mirzania, E., Vishwakarma, D.K., Nivesh, S., Charaan Panda, K., and  Aghaee Daneshvar, F. 2023. A Novel Hybrid Algorithms for Groundwater Level Prediction. Iranian Journal of Science and Technology, Transactions of Civil Engineering. 47(3), 3147–3164
Sebastian, P.A.,  and  Peter, K.V. 2009. Spiders of India. Universities press.2(3), 70-115.
Shin,  S.,  Kyung,  D.,  Lee, S.,  Taik & Kim,  J.,  and  Hyun, J. 2005. An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications.28(4),127-135.
Singh, S. K., Shirzadi, A., and  Pham, B.T. 2021a. Application of artificial intelligence in predicting groundwater contaminants. Water Pollution and Management Practices .8(4),71–105.
Sun, J., Hu, L., Li, D., Sun, K., and Yang, Z. 2022. Data-driven models for accurate groundwater level prediction and their practical significance in groundwater management. Journal of Hydrology. 608(3), 127-142.
Trefry, M. G., and Muffels, C. 2007. FEFLOW: a finite-element ground water flow and transport modeling tool. Groundwater. 45(3), 525–528.
Wang, S., Shao, J., Song, X., Zhang, Y., Huo, Z., and Zhou, X. 2008. Application of MODFLOW and geographic information system to groundwater flow simulation in North China Plain, China. Environmental Earth Sciences. 55(7), 1449-1462
Wang, W., and Ding, J. 2003. Wavelet Network Model and Its Application to the Prediction of Hydrology. Nature and Science.1(1), 67-71.
Environmental Resources Research
Volume 13, Issue 1
July 2025
Pages 119-135

Files

Share

How to cite

Statistics